In many technical applications it is necessary to process an input signal such that in an output signal, audio contents of a useful-signal portion are contained in an essentially unchanged manner relative to the input signal, whereas, on the other hand, audio contents of an interference-signal portion are reduced in the output signal.
Techniques for blind source separation (also referred to as BSS below) have been developed to separate several signals, which are assumed to be statistically independent, from point sources (e.g. voice signals within a room). Respective techniques are described, for example, in publications [1], [2], [3] and [4] (cf. bibliography).
By means of several sensors (e.g. microphones), convolutive mixtures of the point sources (or signals sources) are recorded and demixed using downstream multichannel adaptive filtering. This demixing is based on that the output signals of the multichannel adaptive filtering are to be mutually statistically decoupled again up to statistical moments of a certain order. It is thus the object of blind source separation that ideally, only one of the source signals (i.e. one signal from a point source, or signal source) be applied in each output channel, respectively. However, the disadvantage thereof is that due to the respective one-channel representation at the output after the demixing, the spatial information about the point sources (or signal sources) is lost (in particular level differences and run-time differences between the sensors).
Generally, the object envisaged is to restore spatial information about a spatial location of a point source, or signal source (at the output of a source separation). Several works have already been performed and published in the field mentioned, as will be described below.